U.S. patent number 6,919,964 [Application Number 10/345,814] was granted by the patent office on 2005-07-19 for cd metrology analysis using a finite difference method.
This patent grant is currently assigned to Therma-Wave, Inc.. Invention is credited to Hanyou Chu.
United States Patent |
6,919,964 |
Chu |
July 19, 2005 |
CD metrology analysis using a finite difference method
Abstract
A method for modeling diffraction includes constructing a
theoretical model of the subject. A numerical method is then used
to predict the output field that is created when an incident field
is diffracted by the subject. The numerical method begins by
computing the output field at the upper boundary of the substrate
and then iterates upward through each of the subject's layers.
Structurally simple layers are evaluated directly. More complex
layers are discretized into slices. A finite difference scheme is
performed for these layers using a recursive expansion of the
field-current ratio that starts (or has a base case) at the
lowermost slice. The combined evaluation, through all layers,
creates a scattering matrix that is evaluated to determine the
output field for the subject.
Inventors: |
Chu; Hanyou (San Jose, CA) |
Assignee: |
Therma-Wave, Inc. (Fremont,
CA)
|
Family
ID: |
30118039 |
Appl.
No.: |
10/345,814 |
Filed: |
January 16, 2003 |
Current U.S.
Class: |
356/601; 356/628;
438/16; 702/155 |
Current CPC
Class: |
G03F
7/70625 (20130101) |
Current International
Class: |
G03F
7/20 (20060101); G01B 011/00 () |
Field of
Search: |
;356/601,628,636 ;438/16
;702/155 ;257/E21.53 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
MG. Moharam et al., "Formulation for stable and efficient
implementation of the rigorous coupled-wave analysis of binary
gratings," J. Opt. Soc. Am. A, vol. 12, No. 5, May 1995, pp.
1068-1076. .
Jon Opsal et al., U.S. Appl. No. 09/906,290, filed Jul. 16, 2001,
entitled: "Real Time Analysis of Periodic Structures on
Semiconductors," pp. 1-24, including 3 pages of drawings. .
Yia-Chung Chang et al., U.S. Appl. No. 10/212,385, filed Aug. 5,
2002, entitled: "CD Metrology Analysis Using Green's Function," pp.
1-59, including 2 pages of drawings..
|
Primary Examiner: Rosenberger; Richard A.
Attorney, Agent or Firm: Stallman & Pollock LLP
Parent Case Text
PRIORITY CLAIM
The present application claims priority to U.S. Provisional Patent
Application Ser. No. 60/394,542, filed Jul. 9, 2002, the disclosure
of which is incorporated herein by reference.
Claims
What is claimed is:
1. A method for modeling the diffraction resulting from the
interaction of a probe beam with a subject, where the subject
includes a substrate and one or more layers, the method comprising:
calculating a field-current ratio at the top of the substrate;
recalculating the field-current ratio at the top of each layer of
the subject, beginning with the lowermost layer and ending with the
uppermost layer, the recalculation at each layer performed by: a)
subdividing the layer into a series of horizontal slices; b)
calculating the ratio between the current at the middle of the
uppermost slice and the field at the top of the uppermost slice
using a recursive expansion of the field-current ratio of the
slices between the uppermost and lowermost slices; and c) using an
initial value solver to calculate the field-current ratio at the
top of the uppermost slice.
2. A method as recited in claim 1, that further comprises the step
of using an initial value solver to calculate the current at the
middle of the lowermost slice.
3. A method as recited in claim 1, wherein steps a through c are
performed only for non-uniform layers and where the recalculation
for uniform layers is performed using a numerically exact
solution.
4. A method as recited in claim 1, wherein all of the slices in a
given layer have the same thickness or alternatively non-equally
spaced with a variable transformation.
5. A method for modeling the output field resulting from the
interaction of an incident field with a subject, the method
comprising: using a central difference method with stepping in the
vertical direction to calculate the output field at the upper
boundary of a non-uniform layer within the subject; and correcting
the calculated output field near the upper and lower boundaries of
the non-uniform layer using a current-field relationship.
6. A method as recited in claim 5, that further comprises the step
of calculating a field-current ratio at the top of a substrate
within the subject.
7. A method as recited in claim 5, that further comprises the step
of calculating the output field at the upper boundary of a uniform
layer within the subject using an initial value solver.
8. A method as recited in claim 5, that further comprises the steps
of: subdividing the non-uniform layer into a series of horizontal
slices; calculating the ratio between the current at the middle of
the uppermost slice and the field at the top of the uppermost slice
using a recursive expansion of the field-current ratio of the
slices between the uppermost and lowermost slices; and using an
initial value solver to calculate the field-current ratio at the
upper boundary of the uppermost slice.
9. A method as recited in claim 8, that further comprises the step
of using an initial value solver to calculate the current at the
middle of the lowermost slice.
10. A method for modeling the output field resulting from the
interaction of an incident field with a subject, the method
comprising: using a finite difference method with stepping in the
vertical direction to calculate a scattering matrix for the
subject; and evaluating the scattering matrix using matrix scaling
between the output field and the associated current.
11. A method as recited in claim 10, that further comprises the
step of calculating a field-current ratio at the top of a substrate
within the subject.
12. A method as recited in claim 10, that further comprises the
step of calculating the output field at the upper boundary of a
uniform layer within the subject using an initial value solver.
13. A method as recited in claim 10, that further comprises the
steps of: subdividing a non-uniform layer into a series of
horizontal slices; calculating the ratio between the current at the
middle of the uppermost slice and the field at the top of the
uppermost slice using a recursive expansion of the field-current
ratio of the slices between the uppermost and lowermost slices; and
using an initial value solver to calculate the field-current ratio
at the upper boundary of the uppermost slice.
14. A method as recited in claim 13, that further comprises the
step of using an initial value solver to calculate the current at
the middle of the lowermost slice.
15. A method for modeling the output field resulting from the
interaction of an incident field with a subject, the method
comprising: using a finite difference method with stepping in the
vertical direction to calculate a scattering matrix for the
subject; and using a block tridiagonal UL method to evaluate the
scattering matrix.
16. A method as recited in claim 15, that further comprises the
step of calculating a field-current ratio at the top of a substrate
within the subject.
17. A method as recited in claim 16, that further comprises the
step of calculating the output field at the upper boundary of a
uniform layer within the subject using an initial value solver.
18. A method as recited in claim 16, that further comprises the
steps of: subdividing a non-uniform layer into a series of
horizontal slices; calculating the ratio between the current at the
middle of the uppermost slice and the field at the top of the
uppermost slice using a recursive expansion of the field-current
ratio of the slices between the uppermost and lowermost slices; and
using an initial value solver to calculate the field-current ratio
at the upper boundary of the uppermost slice.
19. A method as recited in claim 18, that further comprises the
step of using an initial value solver to calculate the current at
the middle of the lowermost slice.
20. A method for modeling the output field resulting from the
interaction of an incident field with a subject, the method
comprising: using a pseudo Numerov operator splitting method with
stepping in the vertical direction to calculate a current-field
ratio at the top of a slice within the subject based on the
current-field ratio at the bottom of the slice, to calculate a
scattering matrix for the subject; and evaluating the scattering
matrix.
21. A method as recited in claim 20, that further comprises the
step of calculating a field-current ratio at the top of a substrate
within the subject.
22. A method as recited in claim 20, that further comprises the
steps of: subdividing a non-uniform layer into a series of
horizontal slices; calculating the ratio between the current at the
middle of the uppermost slice and the field at the top of the
uppermost slice using a recursive expansion of the slices between
the uppermost and lowermost slices; and using an initial value
solver to calculate the field-current ratio at the upper boundary
of the uppermost slice.
23. A method as recited in claim 20, that further comprises the
step of using a block tridiagonal UL method to evaluate the
scattering matrix.
24. A method as recited in claim 20, that further comprises the
step of evaluating the scattering matrix using matrix scaling
between the output field and the associated current.
25. A method of optically inspecting and evaluating a subject
comprising the steps of: (a) illuminating the subject with an
incident field; (b) measuring the resulting output field from the
subject to generate at least one empirical reflection coefficient;
(c) defining a hypothetical structure corresponding to the subject;
(d) calculating a predicted reflection coefficient for the
hypothetical structure using a pseudo Numerov method with stepping
in the vertical direction to calculate a scattering matrix for the
subject and evaluating the scattering matrix using matrix scaling
between the output field and the associated current; and (e)
comparing empirical reflection coefficient to the predicted
reflection coefficient to evaluate the subject.
26. A method as recited in claim 25, that further comprises the
step of repeating steps (c) through (e) until the difference
between the predicted reflection coefficient and the empirical
reflection coefficient is minimized.
27. A method as recited in claim 25, that further comprises the
step of calculating the output field at the upper boundary of a
uniform layer within the subject using an initial value solver.
28. A method as recited in claim 25, that further comprises the
steps of: subdividing a non-uniform layer into a series of
horizontal slices; and calculating the ratio between the current
and field at the top of the uppermost slice using an initial value
solver starting from the current-field ratio at the bottom of the
lowermost slice.
29. A method as recited in claim 28, that further comprises the
step of using an initial value solver to calculate the current at
the middle of the lowermost slice.
30. A method as recited in claim 25, that further comprises the
step of using a block tridiagonal UL method to evaluate the
scattering matrix.
31. A method as recited in claim 25, that further comprises the
step of evaluating the scattering matrix using matrix scaling
between the output field and the associated current.
32. A method of optically inspecting and evaluating a subject
comprising the steps of: (a) illuminating the subject with an
incident field; (b) measuring the resulting output field from the
subject to generate at least one empirical reflection coefficient;
(c) defining a hypothetical structure corresponding to the subject;
(d) calculating a predicted reflection coefficient for the
hypothetical structure, wherein said calculation includes a finite
difference analysis; and (e) comparing the empirical reflection
coefficient to the calculated coefficient to evaluate the sample.
Description
TECHNICAL FIELD
The subject invention relates to a technique for numerically
determining the scattering response of a periodic structure using a
finite difference approach.
BACKGROUND OF THE INVENTION
Over the past several years, there has been considerable interest
in using optical scatterometry (i.e., optical diffraction) to
perform critical dimension (CD) measurements of the lines and
structures included in integrated circuits. Optical scatterometry
has been used to analyze periodic two-dimensional structures (e.g.,
line gratings) as well as three-dimensional structures (e.g.,
patterns of vias or mesas). Scatterometry is also used to perform
overlay registration measurements. Overlay measurements attempt to
measure the degree of alignment between successive lithographic
mask layers.
Various optical techniques have been used to perform optical
scatterometry. These techniques include broadband scatterometry
(U.S. Pat. Nos. 5,607,800; 5,867,276 and 5,963,329), spectral
ellipsometry (U.S. Pat. No. 5,739,909) as well as spectral and
single-wavelength beam profile reflectance and beam profile
ellipsometry (U.S. Pat. No. 6,429,943). In addition it may be
possible to employ single-wavelength laser BPR or BPE to obtain CD
measurements on isolated lines or isolated vias and mesas.
Most scatterometry systems use a modeling approach to transform
scatterometry signals into critical dimension measurements. For
this type of approach, a theoretical model is defined for each
physical structure that will be analyzed. A series of calculations
are then performed to predict the empirical measurements (optical
diffraction) that scatterometry systems would record for the
structure. The theoretical results of this calculation are then
compared to the measured data (actually, the normalized data). To
the extent the results do not match, the theoretical model is
modified and the theoretical data is calculated once again and
compared to the empirical measurements. This process is repeated
iteratively until the correspondence between the calculated
theoretical data and the empirical measurements reaches an
acceptable level of fitness. At this point, the characteristics of
the theoretical model and the physical structure should be very
similar.
The most common technique for calculating optical diffraction for
scatterometry models is known as rigorous coupled wave analysis, or
RCWA. For RCWA, the diffraction associated with a model is
calculated by finding solutions to Maxwell's equations for: 1) the
incident electromagnetic field, the electromagnetic field within
the model, and 3) the output or resulting electromagnetic field.
The solutions are obtained by expanding the fields in terms of
exact solutions in each region. The coefficients are obtained by
requiring that the transverse electric and magnetic fields be
continuous (modal matching). Unfortunately, the exact solutions
require repeated matrix diagonalizations in each region in addition
to few other matrix operations. Computationally, matrix
diagonalization is exceedingly slow, generally more than ten times
slower than other matrix operations such as matrix-matrix
multiplications or matrix inversions. As a result, RCWA tends to be
slow, especially for materials with complex dielectric constants.
As the models become more complex (particularly as the profiles of
the walls of the features become more complex) the calculations
become exceedingly long and complex. Even with high-speed
processors, real time evaluation of these calculations can be
difficult. Analysis on a real time basis is very desirable so that
manufacturers can immediately determine when a process is not
operating correctly. The need is becoming more acute as the
industry moves towards integrated metrology solutions wherein the
metrology hardware is integrated directly with the process
hardware.
A number of approaches have been developed to overcome the
calculation bottleneck associated with the analysis of
scatterometry results. Many of these approaches have involved
techniques for improving calculation throughput, such as parallel
processing techniques. An approach of this type is described in a
co-pending application Ser. No. 09/818,703 filed Mar. 27, 2001,
which describes distribution of scatterometry calculations among a
group of parallel processors. In the preferred embodiment, the
processor configuration includes a master processor and a plurality
of slave processors. The master processor handles the control and
the comparison functions. The calculation of the response of the
theoretical sample to the interaction with the optical probe
radiation is distributed by the master processor to itself and the
slave processors.
For example, where the data is taken as a function of wavelength,
the calculations are distributed as a function of wavelength. Thus,
a first slave processor will use Maxwell's equations to determine
the expected intensity of light at selected ones of the measured
wavelengths scattered from a given theoretical model. The other
slave processors will carry out the same calculations at different
wavelengths. Assuming there are five processors (one master and
four slaves) and fifty wavelengths, each processor will perform ten
such calculations per iteration.
Once the calculations are complete, the master processor performs
the best-fit comparison between each of the calculated intensities
and the measured normalized intensities. Based on this fit, the
master processor will modify the parameters of the model as
discussed above (changing the widths or layer thickness). The
master processor will then distribute the calculations for the
modified model to the slave processors. This sequence is repeated
until a good fit is achieved.
This distributed processing approach can also be used with multiple
angle of incidence information. In this situation, the calculations
at each of the different angles of incidence can be distributed to
the slave processor. Techniques of this type are an effective
method for reducing the time required for scatterometry
calculations. At the same time, the speedup provided by parallel
processing is strictly dependent on the availability (and
associated cost) of multiple processors. Amdahl's law also limits
the amount of speedup available by parallel processing since serial
program portions are not improved. At the present time, neither
cost nor ultimate speed improvement is a serious limitation for
parallel processing techniques. As geometries continue to shrink,
however it becomes increasingly possible that computational
complexity will outstrip the use of parallel techniques alone.
Another approach is to use pre-computed libraries of predicted
measurements. This type of approach is discussed in (U.S. Pat. No.
6,483,580) as well as the references cited therein. In this
approach, the theoretical model is parameterized to allow the
characteristics of the physical structure to be varied. The
parameters are varied over a predetermined range and the
theoretical result for each variation to the physical structure is
calculated to define a library of solutions. When the empirical
measurements are obtained, the library is searched to find the best
fit.
The use of libraries speeds up the analysis process by allowing
theoretical results to be computed once and reused many times. At
the same time, library use does not completely solve the
calculation bottleneck. Construction of libraries is time
consuming, requiring repeated evaluation of the same time consuming
theoretical models. Process changes and other variables may require
periodic library modification or replacement at the cost of still
more calculations. For these reasons, libraries are expensive (in
computational terms) to build and to maintain. Libraries are also
necessarily limited in their resolution and can contain only a
finite number of theoretical results. As a result, there are many
cases where empirical measurements do not have exact library
matches. One approach for dealing with this problem is to generate
additional theoretical results in real time to augment the
theoretical results already present in the library. This combined
approach improves accuracy, but slows the scatterometry process as
theoretical models are evaluated in real time. A similar approach
is to use a library as a starting point and apply an interpolation
approach to generate missing results. This approach avoids the
penalty associated with generating results in real time, but
sacrifices accuracy during the interpolation process. See U.S.
application Ser. No. 2002/0038196, incorporated herein by
reference.
For these reasons and others, there is a continuing need for faster
methods for computing theoretical results for scatterometry
systems. This is true both for systems that use real time
evaluation of theoretical models as well as systems that use
library based problem solving. The need for faster evaluation
methods will almost certainly increase as models become more
detailed to more accurately reflect physical structures.
SUMMARY OF THE INVENTION
The following presents a simplified summary of the invention in
order to provide a basic understanding of some of its aspects. This
summary is not an extensive overview of the invention and is
intended neither to identify key or critical elements of the
invention nor to delineate its scope. The primary purpose of this
summary is to present some concepts of the invention in a
simplified form as a prelude to the more detailed description that
is presented later.
The present invention provides a method for modeling optical
diffraction. The modeling method is intended to be used in
combination with a wide range of subjects including semi-conductor
wafers and thin films. A generic subject includes a surface
structure that is covered by an incident medium that is typically
air but may be vacuum, gas, liquid, or solid (such as an overlaying
layer or layers). The surface structure is typically a grating
formed as a periodic series of lines having a defined profile,
width and spacing. In other cases, the surface structure may be an
isolated two or three-dimensional structure such as a single line
or via. Below the surface structure, the subject may include one or
more layers constructed using one or more different materials. At
the bottom of the layers, a typical subject will include a final
layer known as a substrate.
The modeling method begins with the construction of an idealized
representation for the subject being modeled. A numerical method is
then used to predict the output field that is created when an
incident field is diffracted by the subject. The numerical method
begins by computing the output field at the upper boundary of the
substrate. In general, the substrate is fabricated using a uniform
material. This, combined with the boundary conditions (i.e., that
there are only propagating or decaying waves) allows the output
field to be computed using and the uniform material that typically
makes computation of the resulting output field to be computed
directly.
After computation for the substrate is complete, the numerical
method iterates through each of the layers in the subject. The
iteration starts with the lowermost layer and continues to the
grating. For layers that are structurally non-complex (e.g.,
uniform layers) the output field is computed directly. Non-complex
layers also include grating layers that are straight with no
vertical dependence and may be represented using a relatively small
number of slices (e.g., five or fewer).
Layers that are structurally more complex are subdivided into a
series of slices. For each layer, the output field at the upper
boundary of the topmost slice is calculated using a recursive
expansion of the field-current ratio that starts (or has a base
case) at the lowermost slice. The combined evaluation, through all
layers, creates a scattering matrix. Evaluation of the scattering
matrix yields the output field for the subject.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 shows a cross sectional representation of a generic subject
for which optical diffraction may be modeled.
FIG. 2 is a flowchart showing the steps associated with a first
method for modeling optical scattering as provided by an embodiment
of the present invention.
FIG. 3 is a flowchart showing the steps associated with a second
method for modeling optical scattering as provided by an embodiment
of the present invention.
FIG. 4 is a block diagram of an optical metrology system shown as a
representative application for the present invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
Theoretical Foundations
FIG. 1 shows a generic subject 100 of the type typically analyzed
by scatterometry systems. As shown in FIG. 1, the subject 100
includes a surface structure 102. For this particular example,
surface structure 102 is a grating formed as a periodic series of
lines having a defined profile, width and spacing. The grating is
periodic in the X direction and is uniform (exhibits translational
symmetry) along the Y axis. In general, surface structure 102 may
be formed as a wide range of topologies including isolated or
periodic two or three-dimensional structures.
The subject 100 is covered by an incident medium (not shown) that
is typically air but may be vacuum, gas, liquid, or solid (such as
an overlaying layer or layers). Below the grating 102, the subject
100 may include one or more layers constructed using one or more
different materials. In FIG. 1, the internal layers are labeled
104a through 104c. At the bottom of the internal layers 104, the
subject 100 includes a final layer, known as a substrate 106.
For scatterometry systems, the goal is to calculate the
electromagnetic diffraction that results when an incident
electromagnetic field interacts with the subject 100. Cases where
the subject 100 is uniform in the Y direction and the incident
electromagnetic field .psi..sub.in is parallel to the X-Z plane are
described as planer diffraction (this is the case shown in FIG. 1).
Cases where the subject 100 is not uniform over Y (as is the case
where the subject includes a periodic three-dimensional structure)
or the incident electromagnetic field .psi..sub.in is not parallel
to the X-Z plane are described as conical diffraction. In general,
calculation of the planar case is simpler because calculation for
the TE and TM modes may be performed separately. For the conical
case, the TE and TM modes are coupled and must be solved
simultaneously. For both the conical and planar cases,
electromagnetic diffraction is calculated by finding solutions for
Maxwell's equations for: 1) the incident electromagnetic field, the
electromagnetic field within the subject 100, and 3) the output or
resulting electromagnetic field. The solutions for the separate
fields are constrained by the requirement that the TE and TM modes
match at each of the interfaces between the three fields.
In cases where the electric field E(x, z) is expanded in periodic
functions, or, generally speaking for a multi-component second
order differential problem, the following second order differential
equation applies: ##EQU1##
The definition of generalized current J allows the preceding
equation to be rewritten as the following first order differential
equation: ##EQU2##
In the case of planar diffraction, both TE and TM modes are well
defined. For TE modes, p(z) is the identity matrix and for TM modes
p(z) is the inverse dielectric function matrix, the details of
which can be found, for example in M. G. Moharam, E. B. Grann, and
D. A. Pommet, Formulation for stable and efficient implementation
of the rigorous coupled-wave analysis of binary gratings, J. Opt.
Soc. Am. A12, 1068(1995). For conical scattering or scattering by
3D structures, .psi.=(E.sub.x, E.sub.y), after a simple matrix
rotation, this yields: ##EQU3##
where k.sub.x =k.sub.x.vertline.k and k.sub.y =k.sub.y.vertline.k
and k=(k.sub.x.sup.2 +k.sub.y.sup.2).sup.1/2. It is important to
observe that both A and B are symmetric. When the matrix a is real,
it is a special case of a more general class of matrices that are
known as Hamiltonian matrices: ##EQU4##
For planar diffraction, the problem of scattering can be completely
formulated with the following boundary conditions. In the layer and
those underneath, all layers are homogeneous so that the electric
field can be written in diagonal form:
where the f.sub.j are to be determined. The r.sub.j can be
calculated using the recursion relationship for homogeneous
multilayer materials. At the bottom of the grating region, the
electric field can be written (in vector form) as:
where q.ident.ik, r and V are diagonal matrices. Similarly, in the
incident medium, the electric field can be written as: ##EQU5##
where R and w.sub.0 are full matrices. R is the sought after
reflectivity matrix. If w.sub.0 is known, R can be obtained as:
For more general situations, R is obtained as:
##EQU6##
where S is the similarity matrix that diagonalizes BA. S can be
easily obtained for the incident medium.
The idea is that, the exact field at each vertical position z does
not need to be known. Only the matrix ratio between the current J
(H in the case of EM field) and .psi. (E field) needs to be
known.
Initial Value Problem Solver
As will be described below, the numerical solution for the
diffraction problem uses an initial value problem solver. This
section describes a solver that is appropriate for this
application. For this solver, Y is used as to denote the matrix
ratio between the current J and .psi.: ##EQU7##
The solution for Y at z+h in terms of the solution at z can be
written as: ##EQU8##
where T stands for time ordered product. The operator can be
rewritten in terms of Magnus series as: ##EQU9##
All higher order terms involve commutators. .OMEGA.(z+h, z) still
preserves the properties of the Hamiltonian matrices where
D.noteq.0. RCWA corresponds to the following approximation:
and the matrix exponential is calculated exactly via matrix
diagonalization. If a does not depend on z, this approximation is
exact since all commutators become 0. Otherwise RCWA is locally
second order accurate. The next order involves both first order and
second order derivatives. Hence an adaptive grid size can help the
accuracy of the scheme. The evaluation of the operator
e.sup..OMEGA. requires a diagonalization of .OMEGA., which is
numerically expensive. In addition, a straightforward evaluation of
the matrix exponential can be numerically divergent due to
exponentially growing and decaying components. As pointed out
earlier, the crucial observation is that it is sufficient to know
the ratio between J and .psi.. As a matter of fact if the matrix BA
can be diagonalized as:
where .LAMBDA. is diagonal and S is so normalized such that S.sup.T
DS=1, and if J(z)=w(z).psi.(z), it can be shown that: ##EQU10##
Matrix diagonalization is usually more than ten times slower than
matrix-matrix multiplications. As a result, it is worth examining
other alternatives. However, there are three problems one has to
deal with: 1. local accuracy, 2. global numerical instability that
is the result of numerical procedures, and 3. inherent global
instability. As mentioned earlier, RCWA is second order accurate if
a(z) depends on z and it insures good numerical stability as a
result of exact numerical diagonalizations. The third problem is
common to all schemes. Even if e.sup..OMEGA. can be evaluated
exactly, the instability problem remains. This is when the scaling
between J and .psi. comes into play.
Global stability is more important than local accuracy because a
scheme with global instability can render the final results
useless, regardless how good the local accuracy is. One example is
the explicit classical Runge-Kutta method. The following section
describes two alternatives to the RCWA matrix diagonalization
approach.
Central Difference Scheme
For the central difference scheme, each layer in the subject 100 is
divided into N equally spaced segments of height h. The field .psi.
for each segment is denoted: .psi..sub.n.ident..psi.(nh). The field
is located at end points and J located at center points (or vice
versa). As a result of the discretization: ##EQU11##
A simple scheme is to use this relation recursively. In general
this does not work because the resulting matrix eventually
diverges. To overcome this instability, the definition
J.sub.n+1/2.ident.w.sub.n.psi..sub.n is used to produce the
following equation: ##EQU12##
Compared to Equation (10), Equation (11) is more numerically
efficient if w.sub.n is symmetric. For TE modes, B is diagonal and
its inverse is trivial. For TM modes, p is evaluated and B is
calculated as the inverse of p. For non-planar diffraction, B
(which is blockwise diagonal) is evaluated and then inverted. For
the non-planar case, Equation (11) is only marginally faster than
Equation (10).
An equivalent formulation is to eliminate J so that:
By defining the scaling .psi.(z+h)=W(z).psi.(z), the following
recursion is obtained:
W(z-h)=[Ā(z)+p.sup.- +p.sup.- -p.sup.+ W(z)].sup.-1
p.sup.- (12)
where:
The exercise of this is to show that such a scheme is equivalent to
the LU factorization of the block tridiagonal matrix that will be
proven later.
For notational convenience, here and after p denotes p/h, A denotes
Ah, and B denotes Bh. To treat boundaries efficiently a second
order accurate scheme such as the following is used: ##EQU13##
Ignoring AB is first order accurate at the boundary. The resulting
w is symmetric which can make the algorithm almost twice as fast.
With AB included, the results are generally better even if w is
forced to be symmetric.
Operator Splitting
Starting from the equation Y.sub.n-1 =e.sup.-.OMEGA.h Y.sub.n, it
is possible to use Strang splitting which is second order accurate:
##EQU14##
Hence with the use of the second formula, the recursion relation
for w is:
where both A and B are evaluated at center points. This recursion
can be also written as w=p(p+A/2-w).sup.-1 -p-A/2. This recursion
is very similar to the one obtained for the central difference
scheme.
This is also called the leap frog method. It works reasonably well
compared to other schemes such as Runge-Kutta, but not as good as
the central difference scheme described in the previous section.
Part of the reason is that the splitting is not symmetric in terms
of A and B.
Block Tridiagonal UL(LU) Algorithm
Starting with a matrix of the following form: ##EQU15##
It is possible to decompose A=UL, with: ##EQU16##
This produces the relations: ##EQU17##
Defining w.sub.i =d.sub.i.sup.-1 c.sub.i results in: ##EQU18##
The advantage of this approach is that it is sufficient to solve
Lx=y. This is due to the special structure of y where U.sup.-1 y=y.
Since it is sufficient to know the ratio between x.sub.1 and
x.sub.2, there is no need to keep w.sub.i and d.sub.i.
Comparing Equation (12) and Equation (16) shows that the scaling
algorithm described previously (see discussion of the initial value
problem solver) is equivalent to the UL algorithm.
Numerical Procedures for Computing Diffraction
As shown in FIG. 2, the numerical method for computing diffraction
for the subject 100 begins by computing w for the substrate 106
(see step 202). To compute w, BA is diagonalized such that
BA=S.LAMBDA.S.sup.-1. The boundary conditions require that there
are only propagating or decaying waves, with the result that:
In general, the substrate 106 is constructed of a uniform material
making the diagonalization process trivial and S is diagonal.
After computation for the substrate is complete, the numerical
method iterates through each of the remaining layers in the subject
100. The iteration starts with the lowermost layer 104c and
continues through the grating 102. In FIG. 2, this iteration is
controlled by a loop structure formed by steps 204, 206 and 208. In
general, this particular combination of steps is not required and
any suitable iterative control structure may be used.
Within the loop of steps 204 through 208, the numerical method
assesses the complexity of each layer (see step 210). For layers
that are structurally non-complex, Equation (9) is used to obtain a
value for w at the layer's upper boundary (see step 212). Layers of
this type include uniform layers. Non-complex layers also include
grating layers that are straight with no z dependence that may be
represented using a relatively small number of slices (e.g., five
or fewer).
Layers that are structurally more complex are subdivided into a
series of N slices. Each slice has thickness h (see step 214,
variable step size can be achieved by a variable transformation).
Equation (13) is used along with any initial value solver to obtain
the current midway through the lowermost slice (i.e., at (N-1/2)h)
(see step 216).
Equation (11) is then used recursively through the slices to the
top of the layer to obtain the ratio between J.sub.1/2 and
.psi..sub.0 (see step 218). Equation (14) and an initial solver are
then used to obtain the ratio between J.sub.0 and .psi..sub.0 (see
step 220).
After calculating J.sub.0 and .psi..sub.0 for each layer, Equation
(8) is used to obtain the scattered field for the subject 100 (see
step 222).
FIG. 3 shows a variation of the just-described numerical method for
computing diffraction. As may be appreciated by comparison of FIGS.
2 and 3, the variation differs because the N slices of each layer
are no longer required to have the same thickness (compare steps
212 and 314). This allows each layer to be sliced adaptively,
putting more slices in the areas that are the least uniform. The
ability to slice adaptively is accomplished by using Equation (15)
(or other operator splitting scheme) to compute w for each layer
during the iteration process (compare steps 216 through 220 to step
316). This can be viewed as a simple replacement for the
diagonalization procedure RCWA.
In general, any descretization scheme may be used for the numerical
method. Suitable candidates are the so called multi-step backward
difference formulas used to solve stiff differential equations.
Typically, these methods involve more matrix manipulations and are
not symmetric in up and down directions. For TE modes a fourth
order Numerov method may be used. As a matter of fact when the A
and B are independent of the variable z, the only difference
between the central difference and operator splitting is near the
boundaries. A pseudo Numerov method where p is replaced by p-A/12
can be used to greatly enhance the accuracy of the operator
splitting method, reducing the number of slices required
significantly, even surpassing the central difference method.
Representative Application
The numerical method and the associated derivations can be used to
predict the optical scattering produced by a wide range of
structures. FIG. 4 shows the elements of a scatterometer which may
be used to generate empirical measurements for optical scattering.
As shown in FIG. 4, the scatterometer 400 generates a probe beam
402 using an illumination source 404. Depending on the type of
scatterometer 400, the illumination source 404 may be mono or
polychromatic. The probe beam 402 is directed at a subject 406 to
be analyzed. The subject 406 is generally of the type shown in FIG.
1. The reflected probe beam 408 is received by a detector 410. Once
received, changes in reflectivity or polarization state of the
probe beam are measured as a function of angle of incidence or
wavelength (or both) and forwarded to processor 412.
To analyze the changes measured by detector 410, a hypothetical
structure is postulated for the subject 406. The numerical method
is then used to calculate one or more predicted reflection
coefficients for the hypothetical structure. The hypothetical
structure is then changed, and the numerical method repeated, until
the predicted reflection coefficients match the results empirically
observed by detector 410 (within some predetermined goodness of
fit). At this point the hypothetical structure is assumed to
closely match the actual structure of subject 406. In practice, the
numerical method has been found to impart a high degree of
efficiency to this process, allowing the analysis of results in
real or near real-time. The numerical method may also be used to
pre-compute results for a hypothetical structure or for a series of
variations to a hypothetical structure. Typically, the
pre-computing of results is used as part of a library-based
approach where the measurements recorded by detector 410 are
compared (at least initially) to predicted reflection coefficients
that have been computed and stored ahead of time. This sort of
approach may be mixed with the real-time analysis where the
numerical method is used to refine an analysis initially performed
using the library-based approach.
* * * * *